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Foundation Of Learning And Adaptive Systems Question Paper
Foundation Of Learning And Adaptive Systems
Course:Bachelor Of Science In Information Technology
Institution: Masinde Muliro University Of Science And Technology question papers
Exam Year:2010
EXAMINATION FOR THE DEGREE OF BACHELOR OF
SCIENCE IN INFORMATION TECHNOLOGY
BIT 3103: FOUNDATION OF LEARNING AND ADAPTIVE SYSTEMS
DATE: APRIL 2010 TIME: 2 HOURS
INSTRUCTIONS: Answer question ONE and any other TWO questions
QUESTION ONE
a) Explain the meaning and significance of the concept ‘entropy’. (3 Marks)
b) In the ID3 algorithm for constructing decision trees, what is the expected information gain, and
how is it used? (3 Marks)
c) State and explain two strategies that can be used to avoid over fitting in decision trees.(2 Marks)
d) Define the concept of Bayesian learning/networks (4 Marks)
e) Explain the term ‘hypothesis space’ as used in learning theory (2 Marks)
f) Distinguish between supervised learning and unsupervised learning (4Marks)
g) Describe any three applications of supervised learning in business enterprises (3 Marks)
1).What is the initial entropy of GotA? (3 Marks)
2).Which attribute would the decision tree building algorithm choose for the root of the tree? Justify
your answer. (2 Marks)
i) Explain the meaning of the term ‘learning’ as used in artificial intelligence (2 Marks)
j) State and explain two types of neural networks (2 Marks)
QUESTION TWO
a) Briefly explain the meaning of the following terms
i) Machine learning (2 Marks)
ii) Neural Networks (2 Marks)
iii) Neuron (2 Marks)
b) Describe the components of a learning system. Use a diagram to illustrate your answers
(6 Marks)
c) State any two techniques used in Machine Learning. (2 Marks)
d) Briefly explain when the process of computing centroids stops in k- means algorithm ( 2 Marks)
e) A neural network consists of four main parts. Explain each of these parts (4 Marks)
QUESTION THREE
a) Briefly explain the following terms
i) reinforcement learning ( 2 Marks)
ii) percepts ( 2 Marks)
iii) Genetic algorithm ( 2 Marks)
b) Describe three motivations of reinforcement learning ( 3 Marks)
c) State and explain four reinforcement techniques. Use examples to illustrate your answers
(4 Marks)
d) Reinforcement schedules may be used to decrease the probability that a response pattern in a subject
will extinguish. Explain using examples any four reinforcement schedules. ( 4 Marks)
e) Describe any three applications of reinforcement learning ( 3 Marks)
QUESTION FOUR
a) Explain the meaning of the following terms
i) Gene ( 2 Marks)
ii) Allele (2 Marks)
iii) fitness score (2 Marks)
b) Briefly explain four principles of genetic algorithms (4 Marks)
c) After an initial population is randomly generated, the algorithm evolves the through three operators. State and explain each of these operators. Give one example for each case (6 Marks)
d) Describe the steps followed by genetic algorithm in machine learning ( 4 Marks)
QUESTION FIVE
a) Briefly explain the following terms.
i. Concept ( 2 Marks)
ii. Concept-Learning ( 2 Marks)
iii. Training examples (2 Marks)
b) Briefly explain any four possible criteria for evaluating a learning algorithm (4 Marks)
c) Describe four situations that are appropriate Machine learning systems application ( 4 Marks)
d) Describe any three disciplines contributes to machine learning (3 Marks)
e) Briefly explain any three applications of learning and adaptive systems in modern organizations
(3 Marks)
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